the Markowetz lab

Systems Genetics of Cancer: Evolution + Function + Context

2 PhD position: Single cell and spatial cancer genomics

Background - CONTRA (https://itn-contra.org/) is an EU funded Innovative Training Network consisting of 8 PIs from equally many major European universities (see below) as well as partners from pharmaceutical, biotech-start up, and software development companies. CONTRA will hire 15 PhD students, each located and supervised at one of the participating universities. CONTRA will provide training in computational cancer research and professional software development, for analysis of novel experimental data including but not limited to single-cell genomics data.

Collaborations - We will work with the other CONTRA partners and you will have the opportunity to spend time in a partner lab. Partners are: The Royal institute of Technology (KTH, Coordinator), Stockholm; The University of Cambridge; ETH Zurich; University of Warsaw; University of Vigo; King’s College London and the Francis Crick Institute (KCL/Crick); Institute for Research in Biomedicine (IRB); University of Barcelona; The Institute of Cancer Research, London.

Qualifications - Applicants should have now, or plan to have at the end of this academic year, a MSc with excellent results in a computational area (computational biology, bioinformatics, computer science, mathematics, physics, statistics, engineering etc) from a top-ranking university. Applicants with a mixed background (e.g., biological sciences BSc. and bioinformatics Masters) are also encouraged to apply. An applicant must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting university for more than 12 months in the 3 years immediately before the recruitment date. An applicant shall, at the time of recruitment, have received the degree (typically a MSc) entitling him or her to embark on a doctorate no more than 4 years ago.

Application - Will be submitted through a centralised system handled by KTH, which you reach by clicking here.

2 Postdoc positions: Pathways from single-cell CRISPR data (SW14405)

The Markowetz lab at the Cancer Research UK Cambridge Institute is looking for two postdoctoral researchers to work on inferring cellular networks from single cell RNA-seq profiles after CRISPR perturbations.

Background - Several recent high-impact papers introduced experimental techniques for single-cell based genetic screens
to understand gene function and cellular signalling pathways. These techniques combine single-cell
RNA sequencing (scRNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-
based perturbations to massively scale up the resolution and scope of previous genetic screening technologies.
The technology is flexible and will most likely soon be used very widely across molecular biology. As
these technologies are brand-new, tailored computational analysis of these data is lagging behind experimental
advances.

Goals - We will develop a machine learning approach to efficiently analyse scRNA-seq CRISPR screens and
infer gene interaction networks and pathways of information flow in the cell. Our approach is based on
an established machine learning method called Nested Effect Models (NEMs), which has been pioneered
by us. Over the last twelve years NEMs have been refined, extended, and applied by a
world-wide community of independent groups, and now there exists a substantial body of methodological
developments and experience in applications, which we will leverage for the analysis of scRNAseq
CRISPR screens.

Collaborations - We will collaborate with Dr Sarah Teichmann's lab at the Sanger Institute and Dr Christoph Bock's lab at CEMM in Vienna. Working with these leading developers of scRNA-seq CRISPR screens, we will use our methodological advances to optimise the study design of future screens and showcase the power of our approach in collaborative case studies.

Funding - This project is funded by the BBSRC

Skills needed - The successful applicants have strong data analysis skills and experience with genomic data. One postdoc position requires a strong quantitative background in statistics or algorithmics for method development; the second position will focus more application of methods to our collaborators' data sets.